Method

Data from several sources were joined together into a merged dataset. We used 2017 year to build the model. Main outcome is suicide rate for each state, candidate predictors are gun, alchohol, temperature, precipitation, marijuana, education, gdp and gender for each state. We used stepwise approach to select model.

Predictor Description
suicide Suicide rate per 100000 population
gun Number of guns per 1000 population
alcohol Alcohol consumption per capita (gallons of ethanol)
temperature Average temperature (F)
precipitation Average precipitation (inches)
marijuana Marijuana use in adults (%)
education Educational attainment - bachelor’s degree or higher (%)
gdp GDP per capita (dollars)
gender Male (%)

Results

Scatter Plot

Correlation plot

Selected model of interest

term estimate p.value
(Intercept) -143.9870 0.0000
gun 0.1220 0.0007
temperature -0.0955 0.0502
marijuana 0.2485 0.0038
education -0.2343 0.0085
gender 3.5589 0.0000
gdp -0.0002 0.0004
r.squared adj.r.squared AIC BIC
0.8354 0.8124 220.9604 236.2566

The fitted equation is “suicide = -143.99 + 0.12gun - 0.10temperature + 0.25marijuana - 0.23education + 3.56gender - 0.0002gdp”.

As the regression shows, “gun”, “marijuana”,“education”,“gender”,“gdp” are significant predictors for suicide rate. Adjusted R-square is 0.8124, which means these variables can explain a large proportion of variance in the suicide rate. According to the results, suicide rate is larger in states where have a higher gun ownership rate, higher marijuana usage, higher ratio of males to females, lower temperature and lower educational attainment.